Deep Reinforcement Learning-based Multi-objective Path Planning on the Off-road Terrain Environment for Ground Vehicles

Due to the energy-consumption efficiency between up-slope and down-slope is hugely different, a path with the shortest length on a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicles, realizing a good trade-off between distance and energy consumption on 2.5D path planning is significantly meaningful. In this paper, a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP) is proposed. The DMOP can efficiently find the desired path with three steps: (1) Transform the high-resolution 2.5D map into a small-size map. (2) Use a trained deep Q network (DQN) to find the desired path on the small-size map. (3) Build the planned path to the original high-resolution map using a path enhanced method. In addition, the imitation learning method and reward shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, border. Simulation shows that the proposed method can finish the multi-objective 2.5D path planning task. Also, simulation proves that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks on the same map

Psychological symptoms in Chinese nurses may be associated with predisposition to chronic disease: A cross-sectional study of suboptimal health status

© 2020, The Author(s). Background: Suboptimal health status (SHS) is a reversible state between ideal health and illness and it can be effectively reversed by risk prediction, disease prevention, and personalized medicine under the global background of predictive, preventive, and personalized medicine (PPPM) concepts. More and more Chinese nurses have been troubled by psychological symptoms (PS). The correlation between PS and SHS is unclear in nurses. The purpose of current study is to investigate the prevalence of SHS and PS in Chinese nurses and the relationship between SHS and PS along with predisposing factors as well as to discuss the feasibility of improving health status and preventing diseases according to PPPM concepts in Chinese nurses. Methods: A cross-sectional study was conducted with the cluster sampling method among 9793 registered nurses in Foshan city, China. SHS was evaluated with the Suboptimal Health Status Questionnaire-25 (SHSQ-25). Meanwhile, the PS of depression and anxiety were evaluated with Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) self-assessment questionnaires. The relationship between PS and SHS in Chinese nurses was subsequently analyzed. Results: Among the 9793 participants, 6107 nurses were included in the final analysis. The prevalence of SHS in the participants was 74.21% (4532/6107) while the symptoms of depression and anxiety were 47.62% (2908/6107) and 24.59% (1502/6107) respectively. The prevalence of SHS in the participants with depression and anxiety was significantly higher than those without the symptoms of depression (83.3% vs 16.7%, P \u3c 0.001) and anxiety (94.2% vs 5.8%, P \u3c 0.0001). The ratio of exercise habit was significantly lower than that of non-exercise habit (68.8% vs 78.4%, P \u3c 0.001) in SHS group. Conclusions: There is a high prevalence of SHS and PS in Chinese nurses. PS in Chinese nurses are associated with SHS. Physical exercise is a protective factor for SHS and PS so that the exercise should be strongly recommended as a valuable preventive measure well in the agreement with PPPM philosophy. Along with SDS and SAS, SHSQ-25 should also be highly recommended and applied as a novel predictive/preventive tool for the health measures from the perspectives of PPPM in view of susceptible population and individual screening, the predisposition to chronic disease preventing, personalization of intervention, and the ideal health state restoring

High Proportion of 22q13 Deletions and SHANK3 Mutations in Chinese Patients with Intellectual Disability

Intellectual disability (ID) is a heterogeneous disorder caused by chromosomal abnormalities, monogenic factors and environmental factors. 22q13 deletion syndrome is a genetic disorder characterized by severe ID. Although the frequency of 22q13 deletions in ID is unclear, it is believed to be largely underestimated. To address this issue, we used Affymetrix Human SNP 6.0 array to detect the 22q13 deletions in 234 Chinese unexplained ID patients and 103 controls. After the Quality Control (QC) test of raw data, 22q13 deletions were found in four out of 230 cases (1.7%), while absent in parents of the cases and 101 controls. A review of genome-wide microarray studies in ID was performed and the frequency of 22q13 deletions from the literatures was 0.24%, much lower than our report. The overlapping region shared by all 4 cases encompasses the gene SHANK3. A heterozygous de novo nonsense mutation Y1015X of SHANK3 was identified in one ID patient. Cortical neurons were prepared from embryonic mice and were transfected with a control plasmid, shank3 wild-type (WT) or mutant plasmids. Overexpression of the Y1015 mutant in neurons significantly affected neurite outgrowth compared with shank3 WT. These findings suggest that 22q13 deletions may be a more frequent cause for Chinese ID patients than previously thought, and the SHANK3 gene is involved in the neurite development

Deep Reinforcement Learning-Based 2.5D Multi-Objective Path Planning for Ground Vehicles: Considering Distance and Energy Consumption

Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length in a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significantly meaningful. In this paper, we propose a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP). The DMOP can efficiently find the desired path in three steps: (1) transform the high-resolution 2.5D map into a small-size map, (2) use a trained deep Q network (DQN) to find the desired path on the small-size map, and (3) build the planned path to the original high-resolution map using a path-enhanced method. In addition, the hybrid exploration strategy and reward-shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, and border. The simulation results show that the proposed method can finish the multi-objective 2.5D path planning task with significantly high efficiency and quality. Also, simulations prove that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks

Distributed Formation Control of Multi-Robot Systems with Path Navigation via Complex Laplacian

This paper focuses on the formation control of multi-robot systems with leader–follower network structure in directed topology to guide a system composed of multiple mobile robot agents to achieve global path navigation with a desired formation. A distributed linear formation control strategy based on the complex Laplacian matrix is employed, which enables the robot agents to converge into a similar formation of the desired formation, and the size and orientation of the formation are determined by the positions of two leaders. Additionally, in order to ensure that all robot agents in the formation move at a common velocity, the distributed control approach also includes a velocity consensus component. Based on the realization of similar formation control of a multi-robot system, the path navigation algorithm is combined with it to realize the global navigation of the system as a whole. Furthermore, a controller enabling the scalability of the formation size is introduced to enhance the overall maneuverability of the system in specific scenarios like narrow corridors. The simulation results demonstrate the feasibility of the proposed approach

Finite-Time Pinning Synchronization Control for T-S Fuzzy Discrete Complex Networks with Time-Varying Delays via Adaptive Event-Triggered Approach

This paper is concerned with the adaptive event-triggered finite-time pinning synchronization control problem for T-S fuzzy discrete complex networks (TSFDCNs) with time-varying delays. In order to accurately describe discrete dynamical behaviors, we build a general model of discrete complex networks via T-S fuzzy rules, which extends a continuous-time model in existing results. Based on an adaptive threshold and measurement errors, a discrete adaptive event-triggered approach (AETA) is introduced to govern signal transmission. With the hope of improving the resource utilization and reducing the update frequency, an event-based fuzzy pinning feedback control strategy is designed to control a small fraction of network nodes. Furthermore, by new Lyapunov–Krasovskii functionals and the finite-time analysis method, sufficient criteria are provided to guarantee the finite-time bounded stability of the closed-loop error system. Under an optimization condition and linear matrix inequality (LMI) constraints, the desired controller parameters with respect to minimum finite time are derived. Finally, several numerical examples are conducted to show the effectiveness of obtained theoretical results. For the same system, the average triggering rate of AETA is significantly lower than existing event-triggered mechanisms and the convergence rate of synchronization errors is also superior to other control strategies